---
title: "test"
author: "Shenglin Liu"
date: "11/16/2019"
output:
flexdashboard::flex_dashboard:
source_code: embed
vertical_layout: scroll
theme: yeti
---
``` {js}
// Inverse color of navigation bar.
$('.navbar-inverse').removeClass('navbar-inverse').addClass('navbar-default');
```
```{r setup, include=FALSE}
# Load necessary packages.
library(flexdashboard) # Dashboard package
library(highcharter) # Interactive data visualizations
library(plotly) # Interactive data visualizations
library(viridis) # Color gradients
library(tidyverse) # Metapackge
library(countrycode) # Converts country names/codes
library(rjson) # JSON reader
library(crosstalk) # Provides interactivity for HTML widgets
library(DT) # Displaying data tables
```
```{r include=FALSE}
# Read in data.
data <- read.csv('./data/who_suicide_statistics.csv') %>%
filter(year != 2016, # filter out 2016 and countries with 0 data.
country != 'Dominica',
country != 'Saint Kitts and Nevis')
data <- data[complete.cases(data), ]
# Fix the names of some of the countries in our data to match the country names
# used by our map later on so that they'll be interpreted and displayed.
data <- data %>%
mutate(country = fct_recode(country, "The Bahamas" = "Bahamas"),
country = fct_recode(country, "Cape Verde" = "Cabo Verde"),
country = fct_recode(country, "South Korea" = "Republic of Korea"),
country = fct_recode(country, "Russia" = "Russian Federation"),
country = fct_recode(country, "Republic of Serbia" = "Serbia"),
country = fct_recode(country, "United States of America" = "United States"))
# Reorder levels of age to be in chronological order.
data$age <- factor(data$age, levels = c("5-14 years", "15-24 years", "25-34 years", "35-54 years", "55-74 years", "75+ years"))
```
```{r include=FALSE}
# Create a custom theme for the plots.
custom_theme <- hc_theme(
colors = c('#5CACEE', 'green', 'red'),
chart = list(
backgroundColor = '#FAFAFA',
plotBorderColor = "black"),
xAxis = list(
gridLineColor = "E5E5E5",
labels = list(style = list(color = "#333333")),
lineColor = "#E5E5E5",
minorGridLineColor = "#E5E5E5",
tickColor = "#E5E5E5",
title = list(style = list(color = "#333333"))),
yAxis = list(
gridLineColor = "#E5E5E5",
labels = list(style = list(color = "#333333")),
lineColor = "#E5E5E5",
minorGridLineColor = "#E5E5E5",
tickColor = "#E5E5E5",
tickWidth = 1,
title = list(style = list(color = "#333333"))),
title = list(style = list(color = '#333333', fontFamily = "Lato")),
subtitle = list(style = list(color = '#666666', fontFamily = "Lato")),
legend = list(
itemStyle = list(color = "#333333"),
itemHoverStyle = list(color = "#FFF"),
itemHiddenStyle = list(color = "#606063")),
credits = list(style = list(color = "#666")),
itemHoverStyle = list(color = 'gray'))
```
Worldwide
=======================================================================
Column {.tabset .tabset-fade data-width=700 .colored }
-----------------------------------------------------------------------
### Worldwide suicides {.no-padding}
```{r fig.height=5}
# Create tibble for our line plot.
overall_tibble <- data %>%
select(year, suicides_no, population) %>%
group_by(year) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Create a line plot.
highchart() %>%
hc_add_series(overall_tibble, hcaes(x = year, y = suicide_capita, color = suicide_capita), type = "line") %>%
hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: {point.x}
Suicides: {point.y}")) %>%
hc_title(text = "Worldwide suicides by year") %>%
hc_subtitle(text = "1985-2015") %>%
hc_xAxis(title = list(text = "Year")) %>%
hc_yAxis(title = list(text = "Suicides per 100K people"),
allowDecimals = FALSE,
plotLines = list(list(
color = "black", width = 1, dashStyle = "Dash",
value = mean(overall_tibble$suicide_capita),
label = list(text = "Mean = 13.16",
style = list(color = "black", fontSize = 11))))) %>%
hc_legend(enabled = FALSE) %>%
hc_add_theme(custom_theme)
```
### Worldwide suicides by Gender {.no-padding}
```{r fig.height=5.14}
# Create tibble for sex so we can use it when creating our line plot.
sex_tibble <- data %>%
select(year, sex, suicides_no, population) %>%
group_by(year, sex) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Pick color for gender.
sex_color <- c("#EE6AA7", "#87CEEB") # baby blue & pink
# Create line plot.
highchart() %>%
hc_add_series(sex_tibble, hcaes(x = year, y = suicide_capita, group = sex), type = "line", color = sex_color) %>%
hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: {point.x}
","Gender: {point.sex}
", "Suicides: {point.y}")) %>%
hc_title(text = "Worldwide suicides by Gender") %>%
hc_subtitle(text = "1985-2015") %>%
hc_xAxis(title = list(text = "Year")) %>%
hc_yAxis(title = list(text = "Suicides per 100K people"),
allowDecimals = FALSE,
plotLines = list(list(
color = "black", width = 1, dashStyle = "Dash",
value = mean(overall_tibble$suicide_capita),
label = list(text = "Mean = 13.12",
style = list(color = 'black', fontSize = 11))))) %>%
hc_add_theme(custom_theme)
```
### Worldwide suicides by Age {.no-padding}
```{r fig.height=5.13}
# Create tibble for age so we can use it when creating our line plot.
age_tibble <- data %>%
select(year, age, suicides_no, population) %>%
group_by(year, age) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Pick color for graph.
age_color <- rev(plasma(6))
# Create a line plot.
highchart() %>%
hc_add_series(age_tibble, hcaes(x = year, y = suicide_capita, group = age), type = "line", color = age_color) %>%
hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: {point.x}
","Age: {point.age}
", "Suicides: {point.y}")) %>%
hc_title(text = "Worldwide suicides by Age") %>%
hc_subtitle(text = "1985-2015") %>%
hc_xAxis(title = list(text = "Year")) %>%
hc_yAxis(title = list(text = "Suicides per 100K people"),
allowDecimals = FALSE,
plotLines = list(list(
color = "black", width = 1, dashStyle = "Dash",
value = mean(overall_tibble$suicide_capita),
label = list(text = "Mean = 13.12",
style = list(color = 'black', fontSize = 11))))) %>%
hc_add_theme(custom_theme)
```
Column {data-width=300}
-----------------------------------------------------------------------
### Suicides per 100K (1985-2015)
```{r fig.height=0.8}
# Grab worldwide number of suicides per 100K people from the data
total_suicides <- round(mean(overall_tibble$suicide_capita), 2)
# Create value box
valueBox(total_suicides, icon = "fa-plus", color = 'firebrick')
```
### Worldwide suicides by Gender {.no-title .no-padding .colored }
```{r fig.height=1.9}
# First, make a tibble of suicide by sex. We will use this for our pie chart.
pie_sex <- data %>%
select(sex, suicides_no, population) %>%
group_by(sex) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Create pie chart for sex.
highchart() %>%
hc_add_series(pie_sex, hcaes(x = sex, y = suicide_capita,
color = sex_color), type = "pie") %>%
hc_tooltip(borderWidth = 1.5, headerFormat = "", pointFormat = paste("Gender: {point.sex} ({point.percentage:.1f}%)
Suicides per 100K: {point.y}")) %>%
hc_title(text = "Worldwide suicides by Gender", style = (list(fontSize = '14px'))) %>%
hc_subtitle(text = "1985-2015", style = (list(fontSize = '10px'))) %>%
hc_plotOptions(pie = list(dataLabels = list(distance = 5,
style = list(fontSize = 10)),
size = 130)) %>%
hc_add_theme(custom_theme)
```
### Worldwide suicides by Age {.no-title .no-padding .colored }
```{r fig.height=1.9}
# First, create a tibble of suicide by Age. We will use this for our pie chart.
pie_age <- data %>%
select(age, suicides_no, population) %>%
group_by(age) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
arrange(suicide_capita)
# Create pie chart for Age.
highchart() %>%
hc_add_series(pie_age, hcaes(x = age, y = suicide_capita,
color = age_color), type = "pie") %>%
hc_tooltip(borderWidth = 1.5, headerFormat = "", pointFormat = paste("Age: {point.age} ({point.percentage:.1f}%)
Suicides per 100K: {point.y}")) %>%
hc_title(text = "Worldwide suicides by Age", style = (list(fontSize = '14px'))) %>%
hc_subtitle(text = "1985-2015", style = (list(fontSize = '10px'))) %>%
hc_plotOptions(pie = list(dataLabels = list(distance = 5,
style = list(fontSize = 10)),
size = 130)) %>%
hc_add_theme(custom_theme)
```
Continents
========================================================================
Column {data-width=375}
-----------------------------------------------------------------------
```{r include=FALSE}
# Create new column in our data for continent. Use countrycode() to extract continents from country names.
data$continent <- countrycode(sourcevar = data$country,
origin = "country.name",
destination = "continent")
# Reclassify countries that have been coded as 'Americas', by countrycode(), into 'North America' and 'South America'.
south_america <- c('Argentina', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Guyana', 'Paraguay', 'Suriname', 'Uruguay')
data$continent[data$country %in% south_america] <- 'South America'
data$continent[data$continent=='Americas'] <- 'North America'
```
### Suicides by continent and Gender {.no-title .no-padding .colored }
```{r fig.height=2.35}
# Create a tibble for continent and sex.
continent_sex_tibble <- data %>%
select(continent, sex, suicides_no, population) %>%
group_by(continent, sex) %>%
summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Create histogram of suicides by continent.
highchart() %>%
hc_add_series(continent_sex_tibble, hcaes(x = continent, y = suicide_capita, group = sex), type = "column") %>%
hc_colors(colors = sex_color) %>%
hc_title(text = "Suicides by continent and Gender", style = (list(fontSize = '14px'))) %>%
hc_subtitle(text = "1985-2015") %>%
hc_tooltip(borderWidth = 1.5, pointFormat = paste("Gender: {point.sex}
Suicides: {point.y}")) %>%
hc_xAxis(categories = c("Africa", "Asia", "Europe", "North
America", "Oceania", "South
America"), labels = list(style = list(fontSize = 8))) %>%
hc_yAxis(labels = list(style = list(fontSize = 10)),
title = list(text = "Suicides per 100K people",
style = list(fontSize = 10)),
plotLines = list(
list(color = "black", width = 1, dashStyle = "Dash",
value = mean(overall_tibble$suicide_capita),
label = list(text = "Mean = 13.12", style = list(color = "black", fontSize = 6))))) %>%
hc_legend(verticalAlign = 'top', enabled = FALSE) %>%
hc_add_theme(custom_theme)
```
### Suicides by continent and Age {.no-title .no-padding .colored }
```{r fig.height=2.35}
# Create a tibble for continent and sex.
continent_age_tibble <- data %>%
select(continent, age, suicides_no, population) %>%
group_by(continent, age) %>%
summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Create histogram of suicides by continent.
highchart() %>%
hc_add_series(continent_age_tibble, hcaes(x = continent, y = suicide_capita, group = age), type = "column") %>%
hc_colors(colors = age_color) %>%
hc_title(text = "Suicides by continent and Age", style = (list(fontSize = '14px'))) %>%
hc_subtitle(text = "1985-2015") %>%
hc_tooltip(borderWidth = 1.5, pointFormat = paste("Age: {point.age}
Suicides: {point.y}")) %>%
hc_xAxis(categories = c("Africa", "Asia", "Europe", "North
America", "Oceania", "South
America"), labels = list(style = list(fontSize = 8))) %>%
hc_yAxis(labels = list(style = list(fontSize = 10)),
title = list(text = "Suicides per 100K people",
style = list(fontSize = 10)),
plotLines = list(
list(color = "black", width = 1, dashStyle = "Dash",
value = mean(overall_tibble$suicide_capita),
label = list(text = "Mean = 13.12", style = list(color = "black", fontSize = 6))))) %>%
hc_legend(verticalAlign = 'top', enabled = FALSE) %>%
hc_add_theme(custom_theme)
```
Column {data-width=625}
-----------------------------------------------------------------------
### Suicides by continent {.no-title .no-padding .colored }
```{r}
# Import continent map data.
# In order for the map to work on kaggle I had to save the map data and import it.
# This is what is shown as my "private data set". If you want to reproduce this in R you can use the
# commented out code right below it which downloads the map data straight from the internet.
map_data <- download_map_data("custom/world-continents")
# Create a tibble for continent.
continent_tibble <- data %>%
select(continent, suicides_no, population) %>%
group_by(continent) %>%
summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
arrange(suicide_capita)
# Create continent map with suicide data.
highchart() %>%
hc_add_series_map(map_data, continent_tibble, value = "suicide_capita", joinBy = c('name','continent'), name = "Suicides (per 100K people)") %>%
hc_add_series(continent_tibble, hcaes(x = continent, y = suicide_capita, color = suicide_capita), type = "pie", name = 'Suicides (per 100K people)') %>%
hc_colorAxis(stops = color_stops()) %>%
hc_title(text = "Suicides by Continent") %>%
hc_subtitle(text = "1985-2015") %>%
hc_tooltip(borderWidth = 1.5, valueSuffix = '') %>%
hc_plotOptions(
pie = list(center = c('10%', '80%'), size = 110, dataLabels = list(enabled = FALSE))) %>%
hc_add_theme(custom_theme)
```
Countries
========================================================================
Column {.tabset .tabset-fade data-width=400 .colored }
-----------------------------------------------------------------------
### By country {.no-padding}
```{r fig.height=4.55}
# Create tibble for overall suicides by country
country_bar <- data %>%
select(country, suicides_no, population) %>%
group_by(country) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
arrange(desc(suicide_capita))
# Create interactive bar plot
highchart() %>%
hc_add_series(country_bar, hcaes(x = country, y = suicide_capita, color = suicide_capita), type = "bar") %>%
hc_tooltip(borderWidth = 1.5,
pointFormat = paste("Suicides: {point.y}")) %>%
hc_legend(enabled = FALSE) %>%
hc_title(text = "Suicides by country") %>%
hc_subtitle(text = "1985-2015") %>%
hc_xAxis(categories = country_bar$country,
labels = list(step = 1),
min = 0, max = 25,
scrollbar = list(enabled = TRUE)) %>%
hc_yAxis(title = list(text = "Suicides per 100K people")) %>%
hc_plotOptions(bar = list(stacking = "normal",
pointPadding = 0, groupPadding = 0, borderWidth = 0.5)) %>%
hc_add_theme(custom_theme)
```
### By gender {.no-padding}
```{r fig.height=4.65}
# Create tibble for suicide by countries and sex.
country_bar_sex <- data %>%
select(country, sex, suicides_no, population) %>%
group_by(country, sex) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
country_tibble <- data %>%
select(country, suicides_no, population) %>%
group_by(country) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Create bar chart of suicide by sex.
highchart() %>%
hc_add_series(country_bar_sex, hcaes(x = country, y = suicide_capita, group = sex), type = "bar", color = sex_color) %>%
hc_tooltip(borderWidth = 1.5, pointFormat = paste("Gender: {point.sex} ({point.percentage:.1f}%)
Suicides per 100K: {point.y}")) %>%
hc_legend(enabled = TRUE, colorByPoint = TRUE) %>%
hc_title(text = "Suicides by country and gender") %>%
hc_subtitle(text = "1985-2015") %>%
hc_xAxis(categories = country_tibble$country,
labels = list(step = 1),
min = 0, max = 25,
scrollbar = list(enabled = TRUE)) %>%
hc_yAxis(title = list(text = "Percentage of total suicides")) %>%
hc_plotOptions(bar = list(stacking = "percent",
pointPadding = 0, groupPadding = 0, borderWidth = 0.4)) %>%
hc_add_theme(custom_theme)
```
### By age {.no-padding}
```{r fig.height=4.65}
# Create tibble for suicide by countries and age
country_bar_age <- data %>%
select(country, age, suicides_no, population) %>%
group_by(country, age) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Create interactive bar plot.
highchart() %>%
hc_add_series(country_bar_age, hcaes(x = country, y = suicide_capita, group = age), type = "bar", color = age_color) %>%
hc_tooltip(borderWidth = 1.5, pointFormat = paste("Age: {point.age} ({point.percentage:.1f}%)
Suicides per 100K: {point.y}")) %>%
hc_title(text = "Suicides by country and age") %>%
hc_subtitle(text = "1985-2015") %>%
hc_xAxis(categories = country_tibble$country,
labels = list(step = 1),
min = 0, max = 25,
scrollbar = list(enabled = TRUE)) %>%
hc_yAxis(title = list(text = "Percent of total suicides")) %>%
hc_plotOptions(bar = list(stacking = "percent",
pointPadding = 0, groupPadding = 0, borderWidth = 0.5)) %>%
hc_add_theme(custom_theme)
```
Column {data-width=600}
-----------------------------------------------------------------------
### Suicides by country {.no-title .no-padding .colored }
```{r fig.height=4.9}
# Create a tibble with suicide per capita by country for 1985-2015.
country_tibble <- data %>%
select(country, suicides_no, population) %>%
group_by(country) %>%
summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Create interactive world map.
highchart() %>%
hc_add_series_map(worldgeojson, country_tibble, value = "suicide_capita", joinBy = c('name','country')) %>%
hc_colorAxis(stops = color_stops()) %>%
hc_title(text = "Suicides by Country") %>%
hc_subtitle(text = "1985-2015") %>%
hc_tooltip(borderWidth = 1.5, headerFormat = "", valueSuffix = " suicides (per 100K people)") %>%
hc_add_theme(custom_theme)
```
Search {data-icon="fa-search"}
=======================================================================
Column {data-width=250}
-----------------------------------------------------------------------
### Filters {.no-title .colored }
**Pick filters here:**
```{r}
# Create tibble for our line plot.
country_year_tibble <- data %>%
select(country, year, suicides_no, population) %>%
group_by(country, year) %>%
summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
# Create shared data that will be used to link filters, data table, and line plot.
shared_data <- SharedData$new(country_year_tibble, group = 'hello')
# Create filter for year and country. These filters will adjust the DT datatable and PLOTLY plot.
filter_slider("year", "Year", shared_data, ~year, step = 1)
filter_select("country", "Country", shared_data, ~country, allLevels = TRUE, multiple = TRUE)
```
***
**Data table**
```{r}
# Create datatable.
datatable(shared_data,
rownames = FALSE,
colnames = c('Country', 'Year', 'Suicides /100K'),
class = 'cell-border stripe',
extensions = "Scroller",
options=list(deferRender = FALSE,
scrollY = 200,
scrollCollapse = TRUE,
scroller = TRUE,
dom = 't'))
```
Column {data-width=750}
-----------------------------------------------------------------------
### Suicides by country {.no-title .colored }
```{r}
# Set a random seed. We will do this so that we can reproduce the random sample of colors we will use for our next graph.
set.seed(80085)
# Create line graph.
plot_ly(shared_data, x = ~year, y = ~suicide_capita,
color = ~country, colors = sample(colours(), 120),
type = 'scatter', mode = 'lines',
hoverinfo = 'text', text = ~paste("Country: ", country, '
Year: ', year, "
Suicides: ", suicide_capita)) %>%
layout(showlegend = FALSE,
title = "Suicide by country",
xaxis = list(title = "Year"),
yaxis = list(title = "Suicides per 100K people")) %>%
layout(plot_bgcolor = 'transparent') %>%
layout(paper_bgcolor = 'transparent') %>%
add_markers() %>%
highlight("plotly_click")
```